The future of AI is getting a major boost from knowledge graph technology. Think of it like giving AI a dose of “common sense” through structured, up-to-date insights. Knowledge graphs can supercharge AI tools like Large Language Models, machine learning, and artificial neural networks (deep learning). So, knowledge graphs are a way to make AI smarter in context, which is a game changer for businesses, especially when optimizing supply chains. What’s more, it equips AI with factual-based “guardrails” to cut down on errors and hallucinations as well as makes its decisions more trustworthy. With knowledge graphs in the mix, AI becomes more accurate, transparent, and explainable.

In this article, I’ll first highlight the basic components of a knowledge graph and its capabilities. In fact, knowledge graphs are not new. They have already had a major effect on internet search methodologies and how personal assistants work such as Alexa and Siri. Next, I’ll explain how AI developers can leverage knowledge graphs to create contextual-based AI applications. Additionally, I’ll identify the new capabilities that contextual AI brings to businesses. Lastly, I’ll provide nine use cases of what contextual AI can do for supply chains.

“Deep learning presents entirely new opportunities for training neural commonsense models using a massive amount of raw text, fused with symbolic commonsense knowledge graphs.”

Yejin Choi

What Is a Knowledge Graph, and How Has This Technology Made the Internet Smarter?

A knowledge graph is a graphics-based data structure that brings a wealth of diverse information within an interconnected network. Specifically, these networks are where entities such as individuals, places, and objects are nodes linked by edges representing their mutual relationships. Indeed, knowledge graphs have already transformed search engines. Specifically, knowledge graphs provide search engines smarter information retrieval that goes beyond keyword matching to deliver precise, relevant content. See below, for more details on what a knowledge graph is and how it is having a major impact on internet search results and personal assistant chatbots.

1. So, What Is A Knowledge Graph Exactly?

Let’s first start off with a definition.

“Knowledge graphs are defined as graphs of data that accumulate and convey knowledge of the real world. The nodes in knowledge graphs represent the entities of interest, and the edges represent the relations between the entities.”

INDIAai

This definition aptly highlights that a knowledge graph is inherently dynamic rather than static. Knowledge graphs are characterized by their flexibility and robustness, which stem from two main features. Firstly, they are explicitly designed for continual knowledge accumulation. Secondly, they offer a wide application range by being constructed to impart knowledge rather than simply store data. At its core, a knowledge graph comprises three key elements. These include: 

The Components of a Knowledge Graph

• Nodes: These are real-world entities that can be objects, people, events, situations, or abstract concepts.
• Edges: These are the links that connect the nodes.
• Labels: These are the attributes that define the relationships between the nodes and reasoning rules on edges.

2. How Knowledge Graphs Have Made The Internet Smarter.

In particular, academic communities have used knowledge graphs for years, but it was the birth of the internet where this technology started to become very useful. This is because one of the main functions of the internet is information retrieval, and this is something that knowledge graph tech does well. With the introduction of the Google Knowledge Graph in 2012, both academic and business communities began to take great interest in this technology and its use began to expand.

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As the use of knowledge graph technology has expanded, it has become even more useful. For instance, the technology has increased in its capability to gather data from various data sources as well as improve the efficiency of storing graphs. Further, with the use of natural language processing (NLP) it has a better capability to describe and use context in retrieving results. This is evident where voice-based chatbots such as Amazon Alexa, Google Assistant, and Apple Siri leverage knowledge graphs to help answer questions. 

Additionally, we are seeing more uses of knowledge graphs in visual displays such as seen with Google search results panels. Here, the search results not only display links to web sites, but also detailed factual panels related to the search query. See below, for an example of a Google search result knowledge panel. Lastly, the use of knowledge graphs have expanded their use into business such as in the creation of Know Your Customer (KYC) guidelines. With this, more businesses are starting to use knowledge graphs in support of data analytics. In fact, Gartner expects that nearly 80 percent of all data and analytics innovation will include knowledge graphs by 2025.

For more discussion on knowledge graphs and its history, see Altexsoft’s Knowledge Graphs: The Essential Guide.

Moving Toward Contextual AI: 3 Ways A Knowledge Graph Can Help AI Be More Discerning And Contextual.

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Knowledge graphs are the perfect complement to AI’s Large Language Models (LLM). This is because knowledge graphs shore up LLM’s greatest weaknesses. Namely, knowledge graphs help AI to be more accurate, transparent, and explainable. For instance, AI can be less susceptible to hallucinations as knowledge graphs act as guardrails to help keep AI from providing answers that do not line up with the facts. Another thing knowledge graphs do for AI is help to explain its answers instead of just being a “black box”. This transparency and explainability helps us to better trust LLM responses. 

So, it is becoming apparent that we are moving toward a more contextual AI. As a result, this provides incredible capabilities across all industries and use cases. To further detail, below are three ways knowledge graphs are helping AI to become more discerning and contextual.

1. Knowledge Graphs Can Train And Improve LLM Models.

Example: Medical Diagnoses.

Knowledge graphs serve as a structured and comprehensive foundation of real-world facts and relationships that can be used to train Large Language Models (LLMs). For example, let’s look at training a LLM app for medical diagnoses. First, AI developers can use a knowledge graph that contains an interconnected web of symptoms, diseases, medications, and patient histories. As a result, this allows the model to understand the complex relationships and nuances within medical data. So, this depth of understanding enables the AI to provide more accurate and contextually informed diagnoses. Hence, this reduces the likelihood of overlooking critical information.

2. LLMs Can Create Knowledge Graphs.

Example: Environmental Science

Also, LLMs have the capability to interpret and organize vast amounts of unstructured text into structured data, which can then be used to construct a knowledge graph. Take, for instance, the task of creating a knowledge graph for a specific field such as environmental science. A LLM can process thousands of scientific papers, extracting key concepts and their interrelations, such as pollution sources, ecosystem impacts, and conservation strategies. Thus, a LLM can effectively creat a knowledge graph that encapsulates the field’s collective understanding.

3. Knowledge Graphs In Real-time Can Enrich Both LLM’s Queries And Responses.

Examples: Financial Market Analysis

When knowledge graphs are updated in real-time, they can significantly enhance LLMs. Specifically, knowledge graphs can provide AI apps the latest information to inform both the query prompts posed to the AI and the responses it generates. For instance, in the context of financial market analysis, a real-time knowledge graph that includes the latest stock prices, market trends, and news events can help an LLM to deliver more timely and relevant investment insights. As a result, the AI’s responses to queries about potential stock purchases are enriched with the most current data. As a result, this leads to better-informed decision-making for users.

To illustrate, let’s consider an investor who asks an AI app for help to understand what a merger between two tech firms could do. If the AI has current info from a knowledge graph, it can make the question better by adding new market data, past examples of similar mergers, and insights from new articles. This way, when the AI answers the question about the merger, it doesn’t just give basic facts. Indeed, it goes deeper and explains what that merger could mean for the investor’s money.

For more discussion on how knowledge graphs are making AI smarter, see InfoWorld’s How knowledge graphs improve generative AI. Also, for more on AI limitations, see my article, AI Impact On Business Decisions – Know AI’s Unique Challenge To Overcome Its High Number Of Weaknesses.

What New Capabilities Does Contextual AI Offer Businesses?

Indeed, knowledge graphs make LLM much more valuable to business operations and planning. LLMs using knowledge graphs now have new capabilities to be more than personal assistants or to augment internet searches. In fact, contextual AI opens up a wide range of possibilities to businesses. Below are new types of contextual AI capabilities:

• Fact Verification. AI can deliver results that are fact checked.
• Fact Ranking. AI can prioritize results by ranking them against a knowledge graph.
• Related Entities. AI can provide more depth to its results as well as offer better contextual based results.  
• Entity Linking. LLM, AI agents, and users can better put things into context providing better results and references to authoritative content. 

For more discussion on the capabilities that knowledge graphs bring to AI, see GLASP’s The Power of Data, Compute, and Knowledge Graphs in the AI Era. Also, see WiseCube capabilities where they are leveraging knowledge graphs and Large Language Model (LLM) AI to offer research intelligence services for biomedical research organizations.

9 Use Cases For Logistics Organizations To Apply Contextual AI.

To better comprehend how businesses can capitalize on contextual AI and knowledge graphs, consider the domain of supply chain operations and planning. The following use cases clearly demonstrate that knowledge graphs allow Large Language Models to expand their utility far beyond the roles of mere personal assistants or simple data analytics tools. Indeed, these scenarios showcase the potential for logistics organizations to exploit contextual AI’s capabilities in streamlining their workflows, slashing expenses, and boosting overall efficiency

Supply Chain Use Cases For Contextual AI

Data Analysis Examples To Best Overcome The Challenge Of Supply Chains

1. Optimize Supply Chain Routing and Scheduling.

For example, contextual AI can analyze traffic patterns and weather data within real-time knowledge graphs. As a result, this can optimize supply chain routing and scheduling for faster deliveries.

2. Enhance Inventory Management By Anticipating Shifts in Consumer Demand.

For instance, AI can leverage historical sales data and current market trends knowledge graphs. Thus, this enables businesses to better forecast inventory needs and reduce stock outs or overstock situations.

3. Improve Warehouse Planning By Better Predicting Weight, Storage, And Materials Costs.

Contextual AI helps warehouses to enhance space utilization and budgeting. With knowledge graphs, it provides accurate predictions of weight, storage requirements, and material costs based on incoming goods.

4. Have Supply Chain Operations Quickly Adapt to New Compliance Standards.

An up-to-date knowledge graph can help AI to rapidly analyze and interpret changing regulations. Thus, supply chain operations remain compliant without sacrificing efficiency.

5. Personalize Customer Experiences For Last-Mile Delivery.

Logistics firms can employ contextual AI to customize customer’s delivery preferences and times. Thus, this creates tailored last-mile experiences that increase customer satisfaction.

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6. Facilitate Timely, Agile Decision Making Within Distribution Networks.

For example, Contextual AI can help logistics managers to make informed, timely decisions by assessing knowledge graphs of various distribution points and predicting network bottlenecks.

7. Improve Predictive Maintenance for Fleet Vehicles.

By analyzing vehicle performance data and operating conditions, contextual AI can predict when fleet vehicles need maintenance. As a result, this minimizes downtime and extending vehicle life spans.

8. Strengthen Fraud Detection and Security in Logistics Operations.

For instance, contextual AI can use knowledge graphs of facility and environmental characteristics to identify patterns indicative of fraudulent activities.

9. Streamline Data Integration Projects and Data Interoperability Standards Development.

Knowledge graph technology is well suited for supporting, documenting, and adding shared meaning when it comes to data interoperability. As a result, it can both support standards development and data integration implementations. Coupled with AI, knowledge graphs will greatly facilitate the exchange of meaningful data to include using autonomous AI agents in the near future. For more details, see this piece on semantic interoperability, Leverage AI and Knowledge-Centric Tech to Enable Data Standards to Learn, Evolve, and Expand.

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For more references on supply chain use cases for knowledge graph technology, see neo4j’s  Future-Proof Your Supply Chain With Graph Technology. Also, for more business AI use cases applying knowledge graphs, see CES’s In Knowledge Graphs We Trust: Artificial Intelligence 2.0

Conclusion.

So with the use of knowledge graphs, AI software such as Large Language Models (LLM), machine learning, and artificial neural networks (deep learning) are able to become more contextually aware. Indeed, this contextual-based AI is opening up unlimited possibilities for businesses, and in particular, supply chains. As a result, AI can now have factual-based “guardrails” in place to minimize hallucinations and enable us to better trust its results. As a result, AI coupled with knowledge graphs are more accurate, transparent, and explainable.

For additional references on the use of knowledge graphs and AI, see Yejin Choi article, The Curious Case of Commonsense Intelligence and LeewayHertz’s UNDERSTANDING KNOWLEDGE GRAPHS: A KEY TO EFFECTIVE DATA GOVERNANCE, and Daniel McCoy’s From Knowledge Graphs to Knowledge Flows.

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